Comparison · By Chris Sowa · Published
Last updated
What Is the Difference Between RedactSure and Cato Networks Aim Security for Enterprise AI Workflows?
Cato Networks with Aim Security governs AI interactions from the network control point: discovering AI usage, firewalling internal AI applications at runtime and scanning models for misconfiguration. RedactSure governs what the model reads inside a workflow and who approves each consequential action. Treating one as the other leaves either unseen AI usage or a fully visible screen ungoverned.
Cato Networks with Aim Security governs AI interactions from the network control point: discovering AI usage, firewalling internal AI applications at runtime and scanning models for misconfiguration. RedactSure governs what the model reads inside a workflow and who approves each consequential action. Treating one as the other leaves either unseen AI usage or a fully visible screen ungoverned. The question matters because enterprise AI now runs in two places at once: in the traffic between employees and AI services, and inside the applications where agents read records and take actions. A control placed on the first does not automatically reach the second. A buyer who asks "do we have AI security" without asking "at which point" can end up with strong coverage of who is using AI and no answer to what the model saw on a claim file or a vendor master record. Render-layer tokenization answers the second question; network inspection answers the first. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Key findings
- Cato Networks announced its acquisition of Aim Security and describes SASE as the "primary control point for all AI interactions" (Cato Networks, 2026).
- The announcement says the combined capabilities discover shadow AI usage, monitor end-user AI interactions including public AI tools, protect internal AI applications and agents with the Aim AI Firewall, and scan internal AI models with AI-SPM (Cato Networks, 2026).
- The announcement does not state whether the product redacts, masks or tokenizes sensitive data, whether it requires human approval for agent actions, or what it logs (Cato Networks, 2026).
- OWASP lists prompt injection (LLM01) and sensitive information disclosure (LLM02) as leading risks for LLM applications, and both arise from what content reaches the model (OWASP, 2025).
- Researchers at ServiceNow, Mila and partner institutions report near-zero attack success on four agent benchmarks with a firewall that minimizes what reaches tools and sanitizes what comes back, and they warn that public benchmarks are easily saturated (Bhagwatkar et al., arXiv, 2026).
- MIT Technology Review describes layered model defenses as a "Swiss cheese" arrangement in which each layer has holes (MIT Technology Review, 2026).
What does the Cato Networks announcement say Aim Security does?
The source for every statement here about the competitor is one page: Cato Networks' announcement that it acquired Aim Security (Cato Networks, 2026) (vendor announcement, accessed October 14, 2026). It is an acquisition announcement, not product documentation, so it describes direction and capability at a high level.
The announcement names four capabilities. First, discovery: the combined offering "discovers shadow AI usage" and monitors end-user AI interactions, including interactions with public AI tools. Second, runtime protection: the Aim AI Firewall protects internal AI applications and agents against runtime attacks. The vendor's own one-line description reads: "The Aim AI Firewall secures internal AI applications and agents against runtime AI attacks." Third, posture: AI-SPM scans internal AI models for misconfigurations and vulnerabilities. Fourth, visibility: the page claims "complete, 360-degree visibility into AI interactions" across users, AI agents, models, MCP servers and API-driven services.
The announcement places all of this inside Cato's SASE platform, which it calls the "primary control point for all AI interactions." That phrase is the center of the design as described. SASE (secure access service edge) is a network and security service through which user and application traffic passes, so the announcement positions AI governance at the point where traffic flows.
On policy, the announcement says the AI Firewall enforces corporate security and governance policies on interactions between users, AI agents, and internal AI applications and models. On data, it says the capabilities help detect and stop "data breaches."
Several fields a security architect would ask about are absent from the page. The announcement does not state which layers are inspected in detail: network traffic, prompts and responses, browser, endpoint, data stores and identities are not named as inspection points. It does not state how sensitive values are handled, whether by classification, redaction, masking, tokenization or alerting. It does not state whether agent tool calls can be blocked or whether a person approves actions. It does not state what is logged. These are gaps in the source, not findings about the product; product documentation may answer them, and a buyer should request it.
The problem the announcement addresses is real. Shadow AI, AI usage that security teams did not approve and cannot see, is hard to govern because it does not announce itself. A network control point that discovers AI usage gives security teams an inventory they often lack.
How do the two products compare side by side?
The table sets the announcement's statements next to RedactSure's published design. Where the announcement is silent, the table says so rather than inferring.
| Dimension | Cato Networks Aim Security (per vendor announcement, accessed October 14, 2026) | RedactSure (per published design) |
|---|---|---|
| Primary control point | SASE as the "primary control point for all AI interactions" | A governed environment where the agent works the applications; tokenization at the render layer before any model reads the screen |
| What it discovers | Shadow AI usage and end-user AI interactions, including public AI tools | Does not discover AI usage across the network; governs agents a named person connects to the environment |
| What it protects at runtime | Internal AI applications and agents against runtime AI attacks (Aim AI Firewall) | What the model receives: the task's fields, with identifiers as tokens such as SSN_001 and ACCT_001 |
| Posture management | AI-SPM scans internal AI models for misconfigurations and vulnerabilities | Not a posture scanner |
| Sensitive data handling | Helps detect and stop "data breaches"; the announcement does not state redaction, masking or tokenization | Values chosen by policy replaced with consistent tokens; real values resolve only at approved destinations at the moment of an approved action |
| Agent actions | Enforces security and governance policies on interactions; the announcement does not state approvals or tool-call blocking | The Planner sets what the agent may do on each screen; a named person confirms policy and approves every payment, submission and record change |
| Record | The announcement does not state what is logged; it mentions visibility | Every action logged, human and computer; all PII as tokens. Exported to the customer's SIEM in the AI Control Record |
| Position relative to the model and screen | The announcement does not state it | Before the model, at the screen |
| Model choice | The announcement does not state it | Model agnostic; models swap without moving the controls |
| Keys | The announcement does not state it | Customer-held keys; RedactSure holds ciphertext it cannot decrypt |
The two columns answer different questions. Cato's announcement describes coverage across AI interactions: who is using AI, which internal AI applications face attack, which models are misconfigured. RedactSure's design describes control inside a workflow: what an agent sees when it opens a record and who signs off before it acts. An organization can need both, and the table shows little overlap in the stated functions.
Where does each control sit relative to the model and the screen?
Placement decides what a control can see and what it can change. An AI agent security gateway sits on the path between an agent and the services it calls, so it can inspect and filter what passes. A network control point inspects traffic that crosses it. Neither, by position alone, changes what is drawn on a screen that an agent reads inside an application session.
The Cato announcement places governance at SASE and does not state where it sits relative to the model or the user's screen (Cato Networks, 2026). Its descriptions point to interactions: user to public AI tool, user to internal AI application, agent to model, agent to MCP server. These are flows between parties, which is what a network position observes well.
Screen-reading agents present a different flow. When an agent operates an application through its interface, the content it reads is the rendered page. If a claims screen shows a policyholder's name, Social Security number and bank account, the agent's context holds all three unless something changes the page before the model reads it. OWASP's sensitive information disclosure entry describes how data in a model's context can surface in outputs (OWASP, 2025), and Greshake and colleagues showed that content an LLM-integrated application reads can carry instructions that redirect it (Greshake et al., arXiv, 2023).
RedactSure's design acts at that point. The environment replaces values chosen by policy with consistent tokens at the render layer before any model reads the screen, and it hands the model the task's fields rather than a picture of the screen. The model works with PATIENT_001 or CARD_001, and the real value resolves only at an approved destination when a named person approves the action.
The practical difference: a control on traffic can observe and police the conversation between systems; a control at the render layer decides the content of the conversation's input. One does not replace the other. A firewall in front of an internal AI application can block an injection pattern it recognizes. Tokenization at the screen means that an injection which succeeds against the agent collects tokens rather than identifiers.
What happens to a sensitive value in each design?
Take an accounts payable clerk's agent opening a vendor record that shows a routing number and account number, and a remittance email containing a hidden instruction to change the bank details.
In the Cato announcement, the relevant stated functions are the AI Firewall, which enforces security and governance policies on interactions between users, agents and internal AI applications and models, and the claim that the capabilities help detect and stop data breaches (Cato Networks, 2026). The announcement does not state whether the account number is detected, masked or tokenized before an agent reads it, whether the change to bank details would be blocked or held for approval, or whether a record of the event is kept. A buyer evaluating this scenario needs the product documentation to answer each step.
Detection approaches carry known limits. MIT Technology Review reports that vendors stack system prompts, classifiers and probes on internal activations, each with gaps, in the hope that the layers cover one another (MIT Technology Review, 2026). The ServiceNow and Mila study found that a sanitizer at the agent-tool boundary reached near-zero attack success on AgentDojo, Agent Security Bench, InjecAgent and tau-Bench, and also found flawed success metrics, implementation bugs and weak attacks in those benchmarks (Bhagwatkar et al., arXiv, 2026). Its Minimizer, which strips data the tool call does not need, applies the same principle as reducing what reaches the model.
| Step | Cato Networks Aim Security (per announcement) | RedactSure (per design) |
|---|---|---|
| Agent opens the vendor record | Not stated | Routing and account numbers appear to the model as ACCT_001 |
| Email carries hidden instruction | AI Firewall protects internal AI applications and agents against runtime attacks | Instruction may reach the model; it can act only on tokens |
| Agent proposes a bank detail change | Approval not stated | Stops for a named person to approve |
| Record of the event | Not stated | Screen as tokens, action and approval in the AI Control Record |
In RedactSure's design, the account number is a token before the model reads the screen. If the injected instruction persuades the agent to propose a change, the change is a record change, and it stops for a named person under Supervised Delegation. The AI never pays, submits or decides. The person sees the proposed change and the source of the instruction, and approves or rejects it on the record.
Who is being governed, and how is the work recorded?
The Cato announcement frames governance around interactions across the enterprise: users and AI tools, agents and internal applications, models and MCP servers (Cato Networks, 2026). The subject is the organization's AI footprint. The "360-degree visibility" claim describes breadth, and AI-SPM extends that to the condition of internal models. For a CISO who does not yet know how many AI tools employees use, breadth is the first need.
RedactSure frames governance around a single piece of work. A named person grants access, chooses the applications, confirms what is tokenized, can watch and pause the run, and approves every consequential action. The agent is tethered: it works only inside the environment that person connected it to, on the applications that person chose. The subject is the agent's run, and the accountable party is a person with a name.
Records follow from the subject. The announcement does not state what is logged, and mentions visibility into AI usage. RedactSure's AI Control Record holds every screen the agent saw, as tokens, every action and every approval with a name, and it exports to the customer's own SIEM. An auditor reviewing a payment run can see what the agent read, what it proposed and who approved it, without the record itself holding the identifiers.
The NIST AI Risk Management Framework asks organizations to govern, map, measure and manage AI risk (NIST, 2023). Discovery and posture scanning support mapping and measuring across the estate. Per-run approval and a tokenized record support managing a specific workflow and showing, afterward, who was accountable. Microsoft's AI Red Team taxonomy of agentic failure modes includes failures that arise inside an agent's own task execution (Microsoft, 2025), which is where a per-run control applies.
A buyer comparing the two should ask each vendor the same three questions: what does the model read when the agent opens a sensitive record, who approves a consequential action, and what record proves both. The Cato announcement answers none of the three directly; its product documentation may.
Where does RedactSure sit?
RedactSure provides a governed environment where AI co-workers do real work across an organization's applications, with no per-application integration and no change to user permissions. Values chosen by policy are replaced with consistent tokens at the render layer before any model reads the screen, and the model receives the task's fields with identifiers as tokens. The Planner sets which values are tokenized and what the agent may do on each screen; a named person confirms that policy and approves every payment, submission and record change. Real values resolve only at approved destinations at the moment of an approved action. Every screen, as tokens, every action and every approval lands in the AI Control Record and exports to the customer's SIEM. Keys stay with the customer, and the environment is model agnostic. Deployments are in pilot. It does not discover enterprise AI usage or scan model posture.
Everyone else controls where data can go. RedactSure controls what the AI can see.
Methodology and limitations
This page rests on a small set of sources read as of October 13, 2026, with the competitor page accessed October 14, 2026. The description of Cato Networks and Aim Security comes from one vendor announcement of the acquisition, not from product documentation, administrator guides or data sheets. Supporting material comes from standards and official guidance (OWASP Top 10 for LLM Applications 2025, NIST AI Risk Management Framework), research preprints and reports (Greshake et al.; Bhagwatkar et al.; Microsoft AI Red Team), and industry press (MIT Technology Review). RedactSure's column reflects its published current design.
Nothing on this page was tested hands-on. No named product was installed, configured or attacked, and no claim here comes from a trial. Where the announcement does not state something, the page says so and does not infer an answer; absence from an announcement is not evidence that a capability is missing. Vendor features change, product documentation may describe capabilities the announcement omits, and the page carries its date for that reason. No specific organization, customer or deployment is described, and the accounts payable scenario is illustrative.
Whether either product meets a given organization's obligations is not determined here. That determination belongs to the organization's counsel, its assessors and auditors, and its security and compliance officers, working from current product documentation, contracts and their own testing in their own environment.
What the record shows
Cato Networks' announcement positions Aim Security at the network control point, with discovery of shadow AI, a runtime firewall for internal AI applications and agents, and posture scanning for internal models. A workflow control works at a different point: the screen an agent reads and the action it proposes. Relying on a traffic control alone can leave the model reading full identifiers inside an application session; relying on a workflow control alone leaves unapproved AI usage undiscovered. The test: open a sensitive record with the agent and ask each vendor to show what the model received, who approved the resulting action, and the record of both.
Frequently asked questions
Is Cato Networks Aim Security a competitor to RedactSure?
Partly. Both address enterprise AI risk, but at different points. The Cato announcement describes discovery of shadow AI, an AI firewall for internal AI applications and agents, and AI-SPM, all from SASE as the control point. RedactSure governs what the model sees inside a workflow and requires a named person to approve consequential actions. Many organizations would evaluate them for different gaps.
Does Aim Security tokenize or mask sensitive data before a model reads it?
The acquisition announcement does not state it. It says the capabilities help detect and stop data breaches, and it does not mention classification, redaction, masking or tokenization. A buyer should request product documentation that describes how sensitive values are handled at each point. RedactSure's design tokenizes values chosen by policy at the render layer before any model reads the screen.
Can Cato's AI firewall stop a prompt injection hidden in a web page or email?
The announcement says the Aim AI Firewall protects internal AI applications and agents against runtime AI attacks, which includes the category OWASP lists as prompt injection. It does not describe detection methods or results. Research shows detection layers have gaps, so a second control helps: in RedactSure's design, an injection that succeeds collects tokens, and any consequential action still stops for a named person.
Does Aim Security require human approval for agent actions?
The announcement does not state it. It says the AI Firewall enforces corporate security and governance policies on interactions between users, agents and internal AI applications and models, but it does not mention approvals or blocking tool calls. In RedactSure's design, a named person confirms policy before a run and approves every payment, submission and record change while it runs.
Can an organization use both products together?
The stated functions overlap little. A network control point that discovers AI usage and protects internal AI applications addresses breadth across the enterprise. A governed environment that tokenizes the screen and records approvals addresses depth inside specific workflows. An organization could run discovery across its traffic and route sensitive agent work into a governed environment. Confirm integration and logging details with each vendor.
What does RedactSure log that the announcement does not describe?
RedactSure's AI Control Record holds every screen the agent saw, as tokens, every action and every approval with a name, exported to the customer's SIEM. The Cato announcement does not state what is logged; it mentions visibility into AI usage. Ask Cato for logging documentation that shows what an auditor would see for a single agent action.
Related reading
What Is an AI Agent Security Gateway? · What Is an AI Control Record? · What Is Shadow AI and Why Is It So Hard for Security Teams to See? · What Is AI Agent Runtime Security and What Happens After an Agent Gets Access? · What Is the Difference Between RedactSure and Netskope for Sensitive Data Reaching AI Tools? · On the RedactSure blog: The Two Gaps AI Agents Opened in Your Security Stack
Sources
Peer-reviewed research and preprints
- Greshake, Kai, et al., "Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection," arXiv:2302.12173, 2023 (preprint; also AISec 2023). https://arxiv.org/abs/2302.12173
- Bhagwatkar, Kasa, et al., "Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?," arXiv:2510.05244, v2 March 2026 (preprint). https://arxiv.org/abs/2510.05244
- Microsoft AI Red Team, "Taxonomy of Failure Modes in Agentic AI Systems," Microsoft, April 2025 (whitepaper). https://www.microsoft.com/en-us/security/blog/2025/04/24/new-whitepaper-outlines-the-taxonomy-of-failure-modes-in-ai-agents/
Vendor documentation
- Cato Networks, "Cato Acquires Aim Security" (vendor announcement, accessed October 14, 2026). https://www.catonetworks.com/?p=134806
Regulation, standards and official guidance
- OWASP, "LLM01:2025 Prompt Injection" (standards guidance). https://genai.owasp.org/llmrisk/llm01-prompt-injection/
- OWASP, "LLM02:2025 Sensitive Information Disclosure" (standards guidance). https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/
- National Institute of Standards and Technology, "AI Risk Management Framework" (official guidance). https://www.nist.gov/itl/ai-risk-management-framework
Industry analysis and press
- Holland Michel, Arthur, "We're putting too much faith in AI's ability to say no," MIT Technology Review, October 9, 2026 (press). https://www.technologyreview.com/2026/10/09/1145728/we-are-putting-too-much-faith-in-ai-to-say-no/
RedactSure documents
- RedactSure Research, "What Is Render-Layer Tokenization?" (2026). https://redactsure.com/research/what-is-render-layer-tokenization
- RedactSure Research, "What Is Supervised Delegation?" (2026). https://redactsure.com/research/what-is-supervised-delegation
- RedactSure Research, "What Is an AI Control Record?" (2026). https://redactsure.com/research/what-is-an-ai-control-record
- RedactSure Research, "What Is an AI Agent Security Gateway?" (2026). https://redactsure.com/research/what-is-an-ai-agent-security-gateway
- RedactSure Research, "What Is Shadow AI and Why Is It So Hard for Security Teams to See?" (2026). https://redactsure.com/research/what-is-shadow-ai
- RedactSure, "The Two Gaps AI Agents Opened in Your Security Stack" (2026). https://redactsure.com/blog/two-gaps-ai-agents-opened-in-your-security-stack/
- Product behavior described on this page reflects RedactSure's current design. Cato Networks is a trademark of Cato Networks Ltd.; Aim Security is a trademark of its owner; named to identify the products.
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Chris Sowa is a founder of RedactSure and a former CEO of an AI company; he started it years before ChatGPT existed. He previously led AI at Accenture, served as Global VP of Strategy & Innovation at Schneider Electric, was CCO of Sovos, and spent more than a decade at Oracle, with earlier roles at SAP and IBM.